Mammographic computer-aided detection (CAD) systems have existed in research environments since the 1960's. Currently available systems find indicators of cancer in mammograms and present such findings to radiologists. Typical indicators of cancer include clusters of microcalcifications and masses.
Masses are the more difficult cancer indicator for CAD systems to accurately identify. There are several reasons for this. First, there are a wide variety of types of cancerous masses in the breast, ranging from spiculated or stellate masses (henceforth referred to as “spiculations”) to masses with significant core or density (henceforth referred to as “densities”). A visual illustration depicting these various types of cancerous masses can be seen in FIG. 1 of U.S. Pat. No. 6,198,838, “Method and system for detection of suspicious lesions in digital mammograms using a combination of spiculation and density signals,” assigned to R2 Technology, Inc.
As is known in the art, mammographic CAD systems identify cancerous masses by performing two overall steps. First, mass-like regions (i.e., candidates) in the breast are detected at extremely high sensitivity so that all potential cancers (i.e., true positives) are identified. Candidates are typically detected by computing information related to regions in the breast having radiating or converging lines boundaries and/or by computing information related to the shape or boundary of regions in the breast having a defined core or density. By detecting at high sensitivity, a significant number of non-suspicious or benign mass-like regions (i.e., false positives) will also be detected at this first step. Examples of such false positives include lymph nodes, skin folds, skin line, nipples, parenchyma structures, blood vessels, sensor noise, areas of low contrast difference, pacemakers, implants, scars, etc. Like cancerous masses, these structures also exhibit widely varying feature characteristics, making it impossible for detection algorithms alone to avoid the detection of false positives. Thus, mammographic CAD systems perform a second step in which feature measurements are computed on each candidate and collectively used to label each candidate to a class, such as whether the candidate is a true positive that should be presented or whether the candidate is a false positive that should be disregarded. Mammographic CAD systems vary as to the approaches used in this second step. Several notable prior art approaches will now be introduced by way of reference.
In U.S. Pat. No. 6,198,838, Roehrig et al. compute a single vector of feature measurements (i.e., a feature vector) on all mass-like candidates. The vector includes features that are computed on both the radiating and converging lines of the candidate (i.e., spiculated characteristics) and features that are computed on the core or density of each candidate (i.e., dense or mass characteristics). The feature vector is then input to a single classification algorithm, such as a linear classifier or a neural network, that classifies all candidates as either a true positive or false positive. Candidates that have both spiculated and dense characteristics that are well-defined are accurately classified, as the vector input to the algorithm is weighted with a combination of such feature measures. However, it is important to realize that many candidates will have only spiculated characteristics and many other candidates will have only dense characteristics. Furthermore, the single classification algorithm will be limited in the total number of spiculated and dense feature measures it can use, due to limitations associated with the overtraining of classifiers, the type of classification algorithm used, the speed of computational performance, etc. Thus, classification may be performed with suboptimal accuracy on candidates that have only spiculated characteristics and on candidates that have only dense characteristics, resulting in a reduced sensitivity (i.e., a high false negative rate) and/or a high false positive rate (i.e., poor specificity) for such candidates.
In U.S. Pat. No. 7,298,877, “Information fusion with Bayes networks in computer-aided detection systems,” assigned to iCAD, Inc., Collins et al. attempt to overcome the limitations of Roehrig et al. In this patent, a feature vector of spiculated characteristics is computed on candidate masses identified by a spiculation detection algorithm and a feature vector of core characteristics is computed on candidate masses identified by a density detection algorithm. Each feature vector is then input to a separate Bayesian network classification algorithm that classifies each respective set of candidates as either a true positive or false positive. Candidates that have only spiculated characteristics that are well-defined and can be measured by the spiculated feature vector are accurately classified. Furthermore, candidates that have only density characteristics that are well-defined and can be measured by the density feature vector are accurately classified. However, neither Bayesian network classification algorithm classifies candidates that have both spiculated and dense characteristics with optimal accuracy, as neither feature vector characterizes such a combination.
Thus, a mammographic CAD system that classifies all mass-like regions with high sensitivity and at acceptable false positive rates is still desired.
It is therefore an object of this invention to classify mass-like regions exhibiting both spiculated and dense characteristics with high sensitivity and at acceptable false positive rates.
It is another object of this invention to classify mass-like regions exhibiting only dense characteristics with high sensitivity and at acceptable false positive rates.
It is yet a further object of this invention to classify mass-like regions exhibiting only spiculated characteristics with high sensitivity and at acceptable false positive rates.